Meta分析的基础-Cochrane.ppt

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* Usually, it is Marion that participants identify. Point out that it is in fact Jiang that gets the most weight, and that this is because it has a higher event rate, even though Marion has a higher number of participants. * We’ll now go through a forest plot, looking at the various components * Note that when using RevMan you can change the labels at the bottom of the graph, if need be * * Talk about the blob first, and then the CI, then show them the numerical data to the right * It is wrong to add the studies together because it gives the wrong result * An extreme example, where simple addition even makes the result seem to go in the wrong direction. Compare the two approaches * Totally lose the power of randomisation (that it provides comparable groups) Remember that this is the whole reason for choosing this design of study in the first place * In effect we are directly comparing Cooper treatment group with Gaab control group (or any of them) . It makes no sense to go to all the trouble of finding randomised studies only to ignore their design when we come to analyse them. * As Pitts contributes relatively more data to the experimental column, the average death rate in that column is pulled up towards the high level in the Pitts trial more than it is in the control column. * We can look at heterogeneity from two perspectives: the clinical and the statistical point of view. The clinical perspective concerns the qualitative differences between studies, which lead us to expect the studies to give slightly different results. For example the patients may be different. They may have different diagnoses: if the outcome is cancer, different studies may include different tumour types; if the outcome is cardiovascular events, some studies may include angina while others may exclude it. The inclusion and exclusion criteria of studies may differ. The patients may differ in age or sex. If we are interested in interventions for alcohol abuse, it may not make sense t

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